As wind energy is clean, pollution-free and renewable, wind power plays an increasingly important role in the worldwide exploration of new energy. Output power of a wind turbine is constrained by many factors, so it is hard to forecast output power of each wind turbine in a wind farm. In addition, output power of wind turbines has such typical characteristics as being non-linear, fast changing, uncontrollable and the like, so output power of the wind farm to the power grid is prone to fluctuations.
Output power of wind turbines usually depends on meteorological elements of the location of a wind farm. Typically a wind farm is located in a remote area, while meteorological data provided by a meteorological bureau usually fail to cover the surrounding environment of the wind farm. Moreover, meteorological elements at the wind farm are constrained by other conditions (e.g., impacts on airflow of local topographic impact inside the wind farm or rotation of wind turbines themselves, etc.). Even if a meteorological bureau provides a weather forecast in the wind farm area, the weather forecast cannot completely accurately resolve meteorological conditions at the wind farm.
Currently, forecasting of output power of wind turbines mainly focuses on a level of the whole wind farm, and there lacks power forecasting of a single turbine. In addition, forecasting methods rely on analysis and statistics of historical power data and forecast future output power via historical output power or based on meteorological elements at a wind farm by buying general-purpose weather forecasts from a third party.
Solutions in the prior art still rest on commercially available (or free) general meteorological data to forecast output power of wind farms. As these technical solutions ignore special characteristics of in-situ meteorological elements at wind farms, they are prone to large errors during forecasting. On the one hand, errors in power forecasting result in that the overall output power of the power plant becomes unstable, seriously deviates from electricity producing plans and exerts impact on the power grid integration; on the other hand, since output power of the power plant falls behind or goes over far beyond the expected value, the power plant is subjected to punitive sanctions such as fines. Therefore, it becomes a research focus regarding how to accurately forecast output power of a specific wind turbine (e.g., any wind turbine) in a wind farm during a specific period of time.
Therefore, it is desired to develop a technical solution capable of accurately forecasting output power of a specific wind turbine based on meteorological elements at the location of the wind turbine. And it is desired the technical solution is capable of sufficiently leveraging sensors within the wind farm (e.g., meteorological sensors at the wind tower, sensors at hub-height of a wind turbine, etc.), and correcting a general-purpose weather forecasting model by using the meteorological data as measured in-situ, so as to reveal wind information at the specific wind turbine within the wind farm more accurately. To this end, embodiments of the present invention provide methods and apparatuses for forecasting output power of a wind turbine in a wind farm.